ML Architect

About the opportunity:

As a ML Architecht, you set the technical direction for our most complex Generative AI work — and you are personally hands-on building it. We treat AI as a software engineering discipline, and you are the person who raises that bar: architecting systems that survive contact with production, and elevating the engineers around you to do the same.

You own the hardest problems in our portfolio — agentic platforms operating at scale, applied-ML work, and AI in regulated, high-stakes environments where being wrong is expensive. Document intelligence and RAG pipelines are part of the toolkit, not the ceiling. You take these systems from an ambiguous business problem to a robust, scalable production service, solving for the real-world constraints — latency, reliability, cost, governance — that separate a demo from a system a client can bet on.

You are also the technical face of our engagements. You partner directly with client leadership to translate business strategy into AI architecture, shape solutions in pre-sales, and earn the trust that turns a project into a program. This role blends deep, current mastery of LLMs and ML with the judgment, communication, and consultative instinct of a senior technical leader.

What You Will Do:

Set technical direction: Own architecture for our most complex GenAI and agentic systems end-to-end, and set the standards — evaluation, observability, responsible AI — that the practice builds to.

Build at the frontier, hands-on: Stay in the code where it matters most — foundation-model and embedding fine-tuning, novel agentic workflows, advanced RAG and semantic search — using Python on Google Cloud (Vertex AI), LangChain/LlamaIndex, and vector search (Vertex AI Vector Search, Pinecone, pgvector).

Engineer for production: Design for latency, reliability, cost, and scale from day one; apply MLOps discipline so systems are served efficiently, monitored, and continuously improved — and actually reach production, where most AI work stalls.

Lead multi-step reasoning at scale: Architect and operate agentic workflows that automate complex reasoning reliably, with the design and verification discipline that keeps multi-agent systems from cascading into failure.

Advise clients and shape deals: Work directly with client leadership to understand strategy, propose state-of-the-art approaches, and shape solutions in pre-sales — the technical authority in the room.

Multiply the team: Elevate senior and mid-level engineers through architecture reviews, mentorship, and setting a high, teachable bar for AI-augmented engineering.

Your Technical Toolkit:

Core Languages: Mastery of Python and shell scripting; fluency across the modern AI engineering stack.
AI/LLM Ecosystem: Deep, current expertise with Google Gemini, GPT-class, and open models (LLaMA); advanced prompt engineering, fine-tuning, and evaluation.

Agentic AI Engineering: Hands-on experience building autonomous agents and workflows using ADK, LangChain, and custom agent frameworks. 

Agentic Systems: Proven experience designing and productionizing agentic and multi-step reasoning systems (MCP, tool use, orchestration) — not just prototypes.

Data & Search: Expertise in vector databases (Vertex AI Vector Search, pgvector, Pinecone) and semantic search at production scale.

Cloud Infrastructure & AI Systems: End-to-end experience architecting high-availability, scalable software environments across cloud providers (GCP/Vertex AI, AWS, Azure). 

Frameworks: Strong command of LangChain, LlamaIndex, or equivalent orchestration layers — and the judgment to know when not to reach for them.

Engineering foundation: A first-class software engineer — clean, maintainable code, full SDLC ownership, and the architectural judgment to lead others.

Basic Qualifications:

Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.

12+ years in software / AI / ML engineering, with a substantial track record of AI systems delivered to production at scale.

Demonstrated technical leadership — owning architecture and setting direction across engagements or teams, not just individual deliverables.

Proven track record of deploying GenAI and/or agentic products to production environments.

Experience with classic machine learning (neural nets, training, tuning) strongly preferred; foundation-model or novel-model work a distinct plus.

Senior client-facing experience — translating technical complexity into business value for executive stakeholders.

Personal Attributes:

Ownership at scale: You take responsibility not just for your code, but for the outcome of the system and the success of the team building it.

Curiosity: The AI landscape changes weekly; you stay at the frontier and bring the team with you.

Consultative authority: You earn the trust of client leadership and are the calm, credible technical voice in a high-stakes room.

Ethics: You hold the line on responsible AI development and data privacy.